MMP-DCD-CV based sparse channel estimation algorithm for underwater acoustic transform domain communication system

The FPGA design of an adaptive antenna array beamformer is presented. The complex-valued array weights are calculated using the MVDR algorithm whose implementation is based on dichotomous coordinate descent (DCD) iterations. The DCD algorithm allows the multiplication-free solution of the normal equations, resulting in an area-efficient FPGA design that requires approximately 400 slices for the DCD core. Antenna beampatterns obtained from weights calculated in the fixed-point FPGA platform are compared with those of a floating-point simulation. The comparison shows good match of the results for linear arrays of as large as 64 elements. For a 64-element array, the proposed design could provide a weight update rate as high as 28 kHz.

In this paper, we propose a new multichannel filtered-x affine projection algorithm based on dichotomous coordinate descent (DCD) iterations for active noise control (ANC) systems. It includes a fast recursive filtering procedure with the filter update incorporated in the DCD iterations. It is shown that the proposed algorithm has a lower complexity, and superior convergence properties than the multichannel filtered-x LMS algorithm. Also, it compares favorably to a previously published DCD based algorithm for ANC systems.

Underwater acoustic (UWA) communication suffers from the limited available bandwidth of acoustic links. We consider in-band full-duplex (FD) communication in UWA channels, which has the potential of doubling the channel capacity. The key challenge of FD operation is the strong self-interference (SI) introduced by the near-end transmission. For a time-invariant UWA channel, the SI can be efficiently cancelled using an adaptive filter. In practice, the time-varying reflections particularly introduced by the moving sea surface limit the cancellation performance. In this paper, we propose a two-stage SI cancellation scheme with a transceiver structure comprising one projector and two hydrophones. In the first stage, the stable SI path from each hydrophone is cancelled using a digital canceller based on the recursive least-squares (RLS) algorithm with dichotomous coordinate descent (DCD) iterations. In the second stage, the scheme tries to cancel the residual SI using an RLS-DCD-based adaptive beamformer. The beamformer combines the residual signals at the hydrophones in such a way to minimize the final residual SI. The cancellation performance of the proposed scheme is investigated using the Waymark simulator and with lake experiments. As demonstrated by the simulation results, the SI can be efficiently cancelled with the proposed two-stage SI cancellation scheme. The experimental results also demonstrate the capability of the adaptive beamformer in reducing the residual SI after digital cancellation.

In this paper, we propose a recursive least square (RLS) adaptive filter for sparse identification of underwater acoustic (UWA) channels. The adaptive filter is based on sliding window, diagonal loading, and dichotomous coordinate descent iterations. The adaptive algorithm possesses a complexity that is only linear in the filter length. The adaptive filter is used for channel estimation in an UWA communication system with guard-free orthogonal frequency division multiplexing (OFDM) signals and superimposed pilot symbols. We investigate and compare performance of various RLS adaptive filters and show that the proposed sliding window sparse RLS adaptive filter with diagonal loading demonstrates the best performance. We also show that adaptive filters with the sliding window outperform adaptive filters with the exponential window. The comparison has been done using signals recorded in a sea trial at a distance of 80 km transmitted by a fast moving transducer, resulting in fast-varying channel. In these conditions, a low-error-rate transmission is achieved at a data rate of 0.5 bit/s/Hz.

In this paper, the estimation of doubly spread underwater acoustic (UWA) channels is investigated. The UWA channels are characterized by severe delay spread and significant Doppler effects, and can be well modeled as a multi-scale multi-lag (MSML) channel. Furthermore, exploiting the sparsity of UWA channels, MSML channel estimation can be transformed into the estimation of parameter sets (amplitude, Doppler scale factor, time delay). Based on this, the orthogonal matching pursuit (OMP) algorithm has been widely used. However, the estimation accuracy of OMP depends on the size of the dictionary, which is related with both delay spread and Doppler spread. Thus it requires high computational complexity. This paper proposes a new method, called wavelet transform (WT) based algorithm, for the UWA channel estimation. Different from OMP algorithm which needs to search in both time domain and Doppler domain, WT-based algorithm only needs to search in time domain by using the Doppler invariant characteristic of hyperbolic frequency modulation (HFM) signal. The performance of the proposed algorithm is evaluated by computer simulations based on BELLHOP. The simulation results show that WT-based algorithm performs slightly better than OMP algorithm in low signal to noise ratio (SNR) while can greatly reduce computational complexity.

In this paper, a fast orthogonal matching pursuit (OMP) algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic (UWA) channel estimation. The channel estimation consists of calculating amplitude, delay and Doppler scaling factor of each path using the received multi-path signal. This algorithm, called as OIP-FOMP, can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels. In this algorithm, repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance. Efficient QR decomposition is used to estimate the path amplitude, and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix. Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm, without any loss of accuracy.

This paper is concerned with a challenging problem of channel estimation for amplify-and-forward cooperative relay based orthogonal frequency division multiplexing (OFDM) systems in the presence of sparse underwater acoustic channels and of the correlative non-Gaussian noise. We exploit the sparse structure of the channel impulse response to improve the performance of the channel estimation algorithm, due to the reduced number of taps to be estimated. The resulting novel algorithm initially estimates the overall sparse channel taps from the source to the destination as well as their locations using the matching pursuit (MP) approach. The correlated non-Gaussian effective noise is modeled as a Gaussian mixture. Based on the Gaussian mixture model, an efficient and low complexity algorithm is developed based on the combinations of the MP and the space-alternating generalized expectation-maximization (SAGE) technique, to improve the estimates of the channel taps and their location as well as the noise distribution parameters in an iterative way. The proposed SAGE algorithm is designed in such a way that, by choosing the admissible hidden data properly on which the SAGE algorithm relies, a subset of parameters is updated for analytical tractability and the remaining parameters for faster convergence Computer simulations show that underwater acoustic (UWA) channel is estimated very effectively and the proposed algorithm has excellent symbol error rate and channel estimation performance.

The underwater acoustic (UWA) channel causes large propagation delays and reduces the bit error rate (BER) of wireless communication systems. The t-distribution is the optimal distribution to perform UWA noise. In this study, polar-coded differential chaos shift keying (DCSK) and quadrature chaos shift keying (QCSK) communication with UWA noise are considered. First, we have proposed a PDF for the UWA noise channel, and based on this PDF, the theoretical BER is derived. Second, polar coding’s performance is determined to demonstrate the improvement in the BER performance compared to the uncoded UWA system by means of Monte Carlo simulations. The experimental results prove that the nearest model that is applicable to the UWA channel is a t-distribution with five and six degrees of freedom. The BER formulas of the proposed systems are derived and compared with the simulation results. The results confirm the performance improvement of the polar-coded chaotic modulation systems over uncoded systems in UWA channels.

In this paper, we propose an $$l_0$$l0-norm penalized shrinkage linear affine projection ($$l_0$$l0-SL-AP) algorithm and an $$l_0$$l0-norm penalized shrinkage widely linear affine projection ($$l_0$$l0-SWL-AP) algorithm. The proposed algorithms provide variable step-size by minimizing the noise-free a posteriori error at each iteration and introduce an $$l_0$$l0-norm constraint to the cost function. The $$l_0$$l0-SWL-AP algorithm also exploits noncircular properties of the input signal. In contrast with conventional AP algorithms, the proposed algorithms increase the estimation accuracy for time-varying sparse system identification. A quantitative analysis of the convergence behavior for the $$l_0$$l0-SWL-AP algorithm verifies the capabilities of the proposed algorithms. To reduce the complexity, we also introduce dichotomous coordinate descent (DCD) iterations to the proposed algorithms ($$l_0$$l0-SL-DCD-AP and $$l_0$$l0-SWL-DCD-AP) in this paper. Simulations indicate that the $$l_0$$l0-SL-AP and $$l_0$$l0-SWL-AP algorithms provide faster convergence speed and lower steady-state misalignment than the previous APA-type algorithms. The $$l_0$$l0-SL-DCD-AP and $$l_0$$l0-SWL-DCD-AP algorithms perform similarly to their counterparts but with reduced complexity.

To improve the identification capability of AP algorithm in time-varying sparse system, we propose a block parallel l 0 -SWL-DCD-AP algorithm in this paper. In the proposed algorithm, we first introduce the l 0 -norm constraint to promote its application for sparse system. Second, we use the shrinkage denoising method to improve its track ability. Third, we adopt the widely linear processing to take advantage of the non-circular properties of communication signals. Last, to reduce the high computational complexity and make it easy to implemented, we utilize the dichotomous coordinate descent (DCD) iterations and the parallel processing to deal with the tap-weight update in the proposed algorithm. To verify the convergence condition of the proposed algorithm, we also analyze its steady-state behavior. Several simulation are done and results show that the proposed algorithm can achieve a faster convergence speed and a lower steady-state misalignment than similar APA-type algorithm. When apply the proposed algorithm in the decision feedback equalizer (DFE), the bite error rate (BER) decreases obviously.

The fast temporal variations, long multipath delay spreads, and severe frequency-dependent attenuations of underwater acoustic communication channels are extremely complex that impedes underwater acoustic (UWA) data transmission. To alleviate this problem, channel coding is indispensable in UWA communication system to increase the reliability. In this paper, performances of LDPC codes with different parameters over different underwater acoustic communication channels are presented, which is to know how to adjust the encoding and decoding parameters according to different underwater acoustic channels. The results indicate that LDPC codes have a good performance of the estimated BER on the order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> -10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> either in positive sound velocity gradient (PSVG) underwater channel or negative sound velocity gradient (NSVG) one, even invariable sound velocity gradient (ISVG) one. In addition, whatever the encoding or decoding parameters is adopted, performance of the LDPC codes over the NSVG underwater channel is the best, compared with PSVG one and ISVG one. Furthermore, in the desired performance of BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> , the encoding and decoding parameters according to the three different underwater channels specifications are summarized.

Various channel estimation methods have been proposed for shallow underwater communication channel with sparse channel type. A matching pursuit (MP), which is one of typical sparse channel estimation methods, can detect dominant components of sparse channel sequentially as a portion of matching. Mean squared error of MP, however, converges to fixed value even though signal-to-noise ratio (SNR) increases. This paper proposes a minimum mean squared error (MMSE) estimation method with constant false alarm (CFAR) detection for sparse channel estimation. Unlike MP, the mean squared error of the proposed method decreases as SNR increases and is lower than that of conventional MMSE estimators on the sparse channel. Experimental results show that the proposed method estimates sparse underwater communication channel efficiently.

Underwater acoustic (UWA) communication is a critical part of information exchange among marine equipment in the Internet of Underwater Things (IoUT). The impact of the marine environment on UWA communication manifests through physical channels, which exhibit multipath propagation, Doppler spread, and time-varying characteristics. The dynamic compressed sensing (DCS) framework, represented by Kalman Filtered CS (KF-CS) algorithm, is introduced to dynamically adapt support sets by real-time tracking of observations. Meanwhile, channel modeling in Delay-Doppler (DD) domain offers a more stable representation for doubly-selective UWA channels, revealing a potential for channel estimation. In this paper, we transform the time-domain channel estimation into the DD domain channel estimation, and formulate the DD domain channel estimation under the DCS framework to exploit the time-varying sparsity of the UWA channel in the DD domain. In addition, to solve the DD model, the classic algorithms need to search for the sparse solution in the extremely large two-dimensional space of the entire UWA channel. Since only a few multipath arrivals within the large time-delay spread of the entire UWA channel have the Doppler value, we propose the multipath arrivals-Doppler function-based DCS channel estimation approach (DCS-MAD) to solve the DD model. The proposed DCS-MAD can remodel the traditional DD model as multipath arrivals and Doppler model, and search for the sparse solution in the two-dimensional space of multipath arrivals, instead of on the entire channel, so as to effectively reduce system complexity and better focus on Doppler value estimation at the UWA channel’s multipath arrivals. Specifically, we first estimate the multipath arrival delay of the UWA channel using the DCS algorithm. Then, the two-dimensional space search is performed specifically at the multipath arrivals and Doppler domains under the DCS framework to exploit the time-varying sparsity. Finally, the results of numerical simulation and sea experiments validate the superiority of the proposed schemes, compared to the benchmark algorithms.

Under water acoustic (UAC) channel is subjected to large delay spread compared to radio frequency (RF) channels. In order to capture this large delay spread and avoid ISI, the UAC symbol duration needs to be considerably large reducing the transmission data rate. Unlike other RF systems, in Ultra-wideband (UWB) RF systems, small pulse width in the order of nano seconds is used to transmit information. This in turn, gives high resolution multipaths. In a time hopping UWB system, to avoid multipath overlap, the chip duration and the frame duration are kept equal to or larger than the maximum delay spread of the channel. This results in large UWB symbol duration and hence reduces the data rate. In order to increase the data rate, the chip duration, and frame duration can be reduced to less than the maximum delay spread. In addition, to farther increase the data rate, guard interval between consecutive symbols can be reduced/removed allowing consecutive symbols to overlap. This results in intersymbol interference (ISI). Surprisingly, this type of overlap doesn't degrade UWB system performance significantly. In this paper, the same concept is applied to various UAC channels. In order to increase the data rate, consecutive UAC symbols are allowd to overlap. Performance under ISI is then evaluated.

Underwater acoustic channels typically exhibit clustered-sparse structure, and sparse channel recovery based on the clustered-sparse structure can achieve smaller structural error. We propose a novel clustered-sparse Bayesian model for sparse channel estimation in underwater acoustic (UWA) orthogonal frequency-division multiplexing (OFDM) communication systems. In this model, the sparsity and temporal correlation of channel impulse responses (CIRs) are controlled by a set of hyperparameters. For continuously transmitted OFDM blocks in one frame, the auto-regressive (AR) process is used to characterize temporal correlation of time-varying UWA channels, and different clusters are considered to have different AR model coefficients, as they are usually generated according to different transmission routes. A joint cluster partition and sparse Bayesian learning algorithm (JCP-SBL) is proposed to learn the clustered-sparse structure of UWA channels when the cluster partition is unknown. Results of numerical simulation and lake trial demonstrate the effectiveness of the JCP-SBL channel estimator in time-varying UWA channel, which achieve better channel estimation performance and lower bit error rate (BER) compared with the orthogonal matching pursuit (OMP), simultaneous OMP (SOMP) and temporal multiple sparse Bayesian learning (TMSBL).